UniK: Universal Knowledge Perception for Digital and Physical AI

The paper introduces UniK, a universal knowledge perception platform designed to serve both digital AI—such as chatbots and agent workflows—and physical AI, which controls robots and autonomous systems. UniK handles the entire knowledge lifecycle—ingestion, enrichment, indexing, retrieval, and continuous evaluation—across diverse modalities including text, video, molecular data, and sensor telemetry, without task‑specific fine‑tuning. In five digital AI domains, UniK paired with a 70‑billion‑parameter model matches or surpasses larger proprietary LLMs, achieving high retrieval‑augmented generation accuracy on government data, medical QA, and chemistry tasks, and it also addresses similar data challenges in physical AI world‑model training.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• rag
• agents
• robotics
• fine-tuning